





"""simple docstring"""





import argparse
import json
import os

import numpy as np
import PIL
import requests
import tensorflow.keras.applications.efficientnet as efficientnet
import torch
from huggingface_hub import hf_hub_download
from PIL import Image
from tensorflow.keras.preprocessing import image

from transformers import (
    EfficientNetConfig,
    EfficientNetForImageClassification,
    EfficientNetImageProcessor,
)
from transformers.utils import logging


logging.set_verbosity_info()
SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "b0": efficientnet.EfficientNetBa,
    "b1": efficientnet.EfficientNetBa,
    "b2": efficientnet.EfficientNetBa,
    "b3": efficientnet.EfficientNetBa,
    "b4": efficientnet.EfficientNetBa,
    "b5": efficientnet.EfficientNetBa,
    "b6": efficientnet.EfficientNetBa,
    "b7": efficientnet.EfficientNetBa,
}

SCREAMING_SNAKE_CASE__   =  {
    "b0": {
        "hidden_dim": 1_280,
        "width_coef": 1.0,
        "depth_coef": 1.0,
        "image_size": 224,
        "dropout_rate": 0.2,
        "dw_padding": [],
    },
    "b1": {
        "hidden_dim": 1_280,
        "width_coef": 1.0,
        "depth_coef": 1.1,
        "image_size": 240,
        "dropout_rate": 0.2,
        "dw_padding": [16],
    },
    "b2": {
        "hidden_dim": 1_408,
        "width_coef": 1.1,
        "depth_coef": 1.2,
        "image_size": 260,
        "dropout_rate": 0.3,
        "dw_padding": [5, 8, 16],
    },
    "b3": {
        "hidden_dim": 1_536,
        "width_coef": 1.2,
        "depth_coef": 1.4,
        "image_size": 300,
        "dropout_rate": 0.3,
        "dw_padding": [5, 18],
    },
    "b4": {
        "hidden_dim": 1_792,
        "width_coef": 1.4,
        "depth_coef": 1.8,
        "image_size": 380,
        "dropout_rate": 0.4,
        "dw_padding": [6],
    },
    "b5": {
        "hidden_dim": 2_048,
        "width_coef": 1.6,
        "depth_coef": 2.2,
        "image_size": 456,
        "dropout_rate": 0.4,
        "dw_padding": [13, 27],
    },
    "b6": {
        "hidden_dim": 2_304,
        "width_coef": 1.8,
        "depth_coef": 2.6,
        "image_size": 528,
        "dropout_rate": 0.5,
        "dw_padding": [31],
    },
    "b7": {
        "hidden_dim": 2_560,
        "width_coef": 2.0,
        "depth_coef": 3.1,
        "image_size": 600,
        "dropout_rate": 0.5,
        "dw_padding": [18],
    },
}


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		int     ):







      '''simple docstring'''




      lowerCAmelCase    		=			EfficientNetConfig()
      lowerCAmelCase    		=			CONFIG_MAP[model_name]["""hidden_dim"""]
      lowerCAmelCase    		=			CONFIG_MAP[model_name]["""width_coef"""]
      lowerCAmelCase    		=			CONFIG_MAP[model_name]["""depth_coef"""]
      lowerCAmelCase    		=			CONFIG_MAP[model_name]["""image_size"""]
      lowerCAmelCase    		=			CONFIG_MAP[model_name]["""dropout_rate"""]
      lowerCAmelCase    		=			CONFIG_MAP[model_name]["""dw_padding"""]

      lowerCAmelCase    		=			"""huggingface/label-files"""
      lowerCAmelCase    		=			"""imagenet-1k-id2label.json"""
      lowerCAmelCase    		=			10_00
      lowerCAmelCase    		=			json.load(open(hf_hub_download(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		repo_type="""dataset"""     )				,		"""r"""     )     )
      lowerCAmelCase    		=			{int(SCREAMING_SNAKE_CASE     ): v for k, v in idalabel.items()}

      lowerCAmelCase    		=			idalabel
      lowerCAmelCase    		=			{v: k for k, v in idalabel.items()}
      return config


def      UpperCAmelCase__				(    ):







      '''simple docstring'''




      lowerCAmelCase    		=			"""http://images.cocodataset.org/val2017/000000039769.jpg"""
      lowerCAmelCase    		=			Image.open(requests.get(SCREAMING_SNAKE_CASE				,		stream=SCREAMING_SNAKE_CASE     ).raw     )
      return im


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Tuple     ):







      '''simple docstring'''




      lowerCAmelCase    		=			CONFIG_MAP[model_name]["""image_size"""]
      lowerCAmelCase    		=			EfficientNetImageProcessor(
          size={"""height""": size, """width""": size}				,		image_mean=[0.4_85, 0.4_56, 0.4_06]				,		image_std=[0.47_85_39_44, 0.4_73_28_64, 0.47_43_41_63]				,		do_center_crop=SCREAMING_SNAKE_CASE				,		)
      return preprocessor


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		List[str]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			[v.split("""_"""     )[0].split("""block"""     )[1] for v in original_param_names if v.startswith("""block"""     )]
      lowerCAmelCase    		=			sorted(set(SCREAMING_SNAKE_CASE     )     )
      lowerCAmelCase    		=			len(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			{b: str(SCREAMING_SNAKE_CASE     ) for b, i in zip(SCREAMING_SNAKE_CASE				,		range(SCREAMING_SNAKE_CASE     )     )}

      lowerCAmelCase    		=			[]
      rename_keys.append(("""stem_conv/kernel:0""", """embeddings.convolution.weight""")     )
      rename_keys.append(("""stem_bn/gamma:0""", """embeddings.batchnorm.weight""")     )
      rename_keys.append(("""stem_bn/beta:0""", """embeddings.batchnorm.bias""")     )
      rename_keys.append(("""stem_bn/moving_mean:0""", """embeddings.batchnorm.running_mean""")     )
      rename_keys.append(("""stem_bn/moving_variance:0""", """embeddings.batchnorm.running_var""")     )

      for b in block_names:
            lowerCAmelCase    		=			block_name_mapping[b]
            rename_keys.append((F'block{b}_expand_conv/kernel:0', F'encoder.blocks.{hf_b}.expansion.expand_conv.weight')     )
            rename_keys.append((F'block{b}_expand_bn/gamma:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.weight')     )
            rename_keys.append((F'block{b}_expand_bn/beta:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.bias')     )
            rename_keys.append(
                (F'block{b}_expand_bn/moving_mean:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_mean')     )
            rename_keys.append(
                (F'block{b}_expand_bn/moving_variance:0', F'encoder.blocks.{hf_b}.expansion.expand_bn.running_var')     )
            rename_keys.append(
                (F'block{b}_dwconv/depthwise_kernel:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_conv.weight')     )
            rename_keys.append((F'block{b}_bn/gamma:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.weight')     )
            rename_keys.append((F'block{b}_bn/beta:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.bias')     )
            rename_keys.append(
                (F'block{b}_bn/moving_mean:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_mean')     )
            rename_keys.append(
                (F'block{b}_bn/moving_variance:0', F'encoder.blocks.{hf_b}.depthwise_conv.depthwise_norm.running_var')     )

            rename_keys.append((F'block{b}_se_reduce/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.weight')     )
            rename_keys.append((F'block{b}_se_reduce/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.reduce.bias')     )
            rename_keys.append((F'block{b}_se_expand/kernel:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.weight')     )
            rename_keys.append((F'block{b}_se_expand/bias:0', F'encoder.blocks.{hf_b}.squeeze_excite.expand.bias')     )
            rename_keys.append(
                (F'block{b}_project_conv/kernel:0', F'encoder.blocks.{hf_b}.projection.project_conv.weight')     )
            rename_keys.append((F'block{b}_project_bn/gamma:0', F'encoder.blocks.{hf_b}.projection.project_bn.weight')     )
            rename_keys.append((F'block{b}_project_bn/beta:0', F'encoder.blocks.{hf_b}.projection.project_bn.bias')     )
            rename_keys.append(
                (F'block{b}_project_bn/moving_mean:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_mean')     )
            rename_keys.append(
                (F'block{b}_project_bn/moving_variance:0', F'encoder.blocks.{hf_b}.projection.project_bn.running_var')     )

      rename_keys.append(("""top_conv/kernel:0""", """encoder.top_conv.weight""")     )
      rename_keys.append(("""top_bn/gamma:0""", """encoder.top_bn.weight""")     )
      rename_keys.append(("""top_bn/beta:0""", """encoder.top_bn.bias""")     )
      rename_keys.append(("""top_bn/moving_mean:0""", """encoder.top_bn.running_mean""")     )
      rename_keys.append(("""top_bn/moving_variance:0""", """encoder.top_bn.running_var""")     )

      lowerCAmelCase    		=			{}
      for item in rename_keys:
            if item[0] in original_param_names:
                  lowerCAmelCase    		=			"""efficientnet.""" + item[1]

      lowerCAmelCase    		=			"""classifier.weight"""
      lowerCAmelCase    		=			"""classifier.bias"""
      return key_mapping


def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any				,		SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		List[Any]     ):







      '''simple docstring'''




      for key, value in tf_params.items():
            if "normalization" in key:
                  continue

            lowerCAmelCase    		=			key_mapping[key]
            if "_conv" in key and "kernel" in key:
                  lowerCAmelCase    		=			torch.from_numpy(SCREAMING_SNAKE_CASE     ).permute(3				,		2				,		0				,		1     )
            elif "depthwise_kernel" in key:
                  lowerCAmelCase    		=			torch.from_numpy(SCREAMING_SNAKE_CASE     ).permute(2				,		3				,		0				,		1     )
            elif "kernel" in key:
                  lowerCAmelCase    		=			torch.from_numpy(np.transpose(SCREAMING_SNAKE_CASE     )     )
            else:
                  lowerCAmelCase    		=			torch.from_numpy(SCREAMING_SNAKE_CASE     )

            # Replace HF parameters with original TF model parameters
            assert hf_params[hf_key].shape == new_hf_value.shape
            hf_params[hf_key].copy_(SCREAMING_SNAKE_CASE     )


@torch.no_grad()
def      UpperCAmelCase__				(    SCREAMING_SNAKE_CASE	:		Any				,		SCREAMING_SNAKE_CASE	:		Any				,		SCREAMING_SNAKE_CASE	:		List[Any]				,		SCREAMING_SNAKE_CASE	:		Optional[Any]     ):







      '''simple docstring'''




      lowerCAmelCase    		=			model_classes[model_name](
          include_top=SCREAMING_SNAKE_CASE				,		weights="""imagenet"""				,		input_tensor=SCREAMING_SNAKE_CASE				,		input_shape=SCREAMING_SNAKE_CASE				,		pooling=SCREAMING_SNAKE_CASE				,		classes=10_00				,		classifier_activation="""softmax"""				,		)

      lowerCAmelCase    		=			original_model.trainable_variables
      lowerCAmelCase    		=			original_model.non_trainable_variables
      lowerCAmelCase    		=			{param.name: param.numpy() for param in tf_params}
      for param in tf_non_train_params:
            lowerCAmelCase    		=			param.numpy()
      lowerCAmelCase    		=			list(tf_params.keys()     )

      # Load HuggingFace model
      lowerCAmelCase    		=			get_efficientnet_config(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			EfficientNetForImageClassification(SCREAMING_SNAKE_CASE     ).eval()
      lowerCAmelCase    		=			hf_model.state_dict()

      # Create src-to-dst parameter name mapping dictionary
      print("""Converting parameters..."""     )
      lowerCAmelCase    		=			rename_keys(SCREAMING_SNAKE_CASE     )
      replace_params(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE     )

      # Initialize preprocessor and preprocess input image
      lowerCAmelCase    		=			convert_image_processor(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			preprocessor(images=prepare_img()				,		return_tensors="""pt"""     )

      # HF model inference
      hf_model.eval()
      with torch.no_grad():
            lowerCAmelCase    		=			hf_model(**SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			outputs.logits.detach().numpy()

      # Original model inference
      lowerCAmelCase    		=			False
      lowerCAmelCase    		=			CONFIG_MAP[model_name]["""image_size"""]
      lowerCAmelCase    		=			prepare_img().resize((image_size, image_size)				,		resample=PIL.Image.NEAREST     )
      lowerCAmelCase    		=			image.img_to_array(SCREAMING_SNAKE_CASE     )
      lowerCAmelCase    		=			np.expand_dims(SCREAMING_SNAKE_CASE				,		axis=0     )
      lowerCAmelCase    		=			original_model.predict(SCREAMING_SNAKE_CASE     )

      # Check whether original and HF model outputs match  -> np.allclose
      assert np.allclose(SCREAMING_SNAKE_CASE				,		SCREAMING_SNAKE_CASE				,		atol=1e-3     ), "The predicted logits are not the same."
      print("""Model outputs match!"""     )

      if save_model:
            # Create folder to save model
            if not os.path.isdir(SCREAMING_SNAKE_CASE     ):
                  os.mkdir(SCREAMING_SNAKE_CASE     )
            # Save converted model and image processor
            hf_model.save_pretrained(SCREAMING_SNAKE_CASE     )
            preprocessor.save_pretrained(SCREAMING_SNAKE_CASE     )

      if push_to_hub:
            # Push model and image processor to hub
            print(F'Pushing converted {model_name} to the hub...'     )
            lowerCAmelCase    		=			F'efficientnet-{model_name}'
            preprocessor.push_to_hub(SCREAMING_SNAKE_CASE     )
            hf_model.push_to_hub(SCREAMING_SNAKE_CASE     )


if __name__ == "__main__":
       SCREAMING_SNAKE_CASE__   =  argparse.ArgumentParser()
       # Required parameters
       parser.add_argument(
           "--model_name",
           default="b0",
           type=str,
           help="Version name of the EfficientNet model you want to convert, select from [b0, b1, b2, b3, b4, b5, b6, b7].",
       )
       parser.add_argument(
           "--pytorch_dump_folder_path",
           default="hf_model",
           type=str,
           help="Path to the output PyTorch model directory.",
       )
       parser.add_argument("--save_model", action="store_true", help="Save model to local")
       parser.add_argument("--push_to_hub", action="store_true", help="Push model and image processor to the hub")

       SCREAMING_SNAKE_CASE__   =  parser.parse_args()
       convert_efficientnet_checkpoint(args.model_name, args.pytorch_dump_folder_path, args.save_model, args.push_to_hub)













"""simple docstring"""





import os
import shutil
from pathlib import Path
from typing import Optional, Union

import numpy as np
from huggingface_hub import hf_hub_download

from ..utils import ONNX_EXTERNAL_WEIGHTS_NAME, ONNX_WEIGHTS_NAME, is_onnx_available, logging


if is_onnx_available():
       import onnxruntime as ort


SCREAMING_SNAKE_CASE__   =  logging.get_logger(__name__)

SCREAMING_SNAKE_CASE__   =  {
    "tensor(bool)": np.bool_,
    "tensor(int8)": np.inta,
    "tensor(uint8)": np.uinta,
    "tensor(int16)": np.intaa,
    "tensor(uint16)": np.uintaa,
    "tensor(int32)": np.intaa,
    "tensor(uint32)": np.uintaa,
    "tensor(int64)": np.intaa,
    "tensor(uint64)": np.uintaa,
    "tensor(float16)": np.floataa,
    "tensor(float)": np.floataa,
    "tensor(double)": np.floataa,
}

class 						lowercase   :





      def __init__(			self ,		lowercase=None ,		**lowercase					)						->	int:
            logger.info("""`diffusers.OnnxRuntimeModel` is experimental and might change in the future."""					)
            lowerCAmelCase    		=			model
            lowerCAmelCase    		=			kwargs.get("""model_save_dir""" ,		lowercase					)
            lowerCAmelCase    		=			kwargs.get("""latest_model_name""" ,		lowercase					)





      def __call__(			self ,		**lowercase					)						->	List[Any]:
            lowerCAmelCase    		=			{k: np.array(lowercase					) for k, v in kwargs.items()}
            return self.model.run(lowercase ,		lowercase					)





      @staticmethod
      def        _snake_case     (			lowercase ,		lowercase=None ,		lowercase=None					)						->	Optional[Any]:
            if provider is None:
                  logger.info("""No onnxruntime provider specified, using CPUExecutionProvider"""					)
                  lowerCAmelCase    		=			"""CPUExecutionProvider"""

            return ort.InferenceSession(lowercase ,		providers=[provider] ,		sess_options=lowercase					)





      def        _snake_case     (			self ,		lowercase ,		lowercase = None ,		**lowercase					)						->	str:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME

            lowerCAmelCase    		=			self.model_save_dir.joinpath(self.latest_model_name					)
            lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
            try:
                  shutil.copyfile(lowercase ,		lowercase					)
            except shutil.SameFileError:
                  pass

            # copy external weights (for models >2GB)
            lowerCAmelCase    		=			self.model_save_dir.joinpath(lowercase					)
            if src_path.exists():
                  lowerCAmelCase    		=			Path(lowercase					).joinpath(lowercase					)
                  try:
                        shutil.copyfile(lowercase ,		lowercase					)
                  except shutil.SameFileError:
                        pass





      def        _snake_case     (			self ,		lowercase ,		**lowercase ,		)						->	str:
            if os.path.isfile(lowercase					):
                  logger.error(f'Provided path ({save_directory}) should be a directory, not a file'					)
                  return

            os.makedirs(lowercase ,		exist_ok=lowercase					)

            # saving model weights/files
            self._save_pretrained(lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = None ,		lowercase = None ,		lowercase = False ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	Union[str, Any]:
            lowerCAmelCase    		=			file_name if file_name is not None else ONNX_WEIGHTS_NAME
            # load model from local directory
            if os.path.isdir(lowercase					):
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(
                      os.path.join(lowercase ,		lowercase					) ,		provider=lowercase ,		sess_options=lowercase					)
                  lowerCAmelCase    		=			Path(lowercase					)
            # load model from hub
            else:
                  # download model
                  lowerCAmelCase    		=			hf_hub_download(
                      repo_id=lowercase ,		filename=lowercase ,		use_auth_token=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		)
                  lowerCAmelCase    		=			Path(lowercase					).parent
                  lowerCAmelCase    		=			Path(lowercase					).name
                  lowerCAmelCase    		=			OnnxRuntimeModel.load_model(lowercase ,		provider=lowercase ,		sess_options=lowercase					)
            return cls(model=lowercase ,		**lowercase					)





      @classmethod
      def        _snake_case     (			cls ,		lowercase ,		lowercase = True ,		lowercase = None ,		lowercase = None ,		**lowercase ,		)						->	List[str]:
            lowerCAmelCase    		=			None
            if len(str(lowercase					).split("""@"""					)					) == 2:
                  lowerCAmelCase		,						lowerCAmelCase    		=			model_id.split("""@"""					)

            return cls._from_pretrained(
                model_id=lowercase ,		revision=lowercase ,		cache_dir=lowercase ,		force_download=lowercase ,		use_auth_token=lowercase ,		**lowercase ,		)






